Vector quantized optimal stage wise video frame classifier for human face recognition.

Video frame analytic systems are receiving an increasing interest, not only for automatic detection of abnormal events, but also enforcing human face recognition. Recently, many research works have been designed for video frame analytic system. They may fail to reject an operator which gradually builds and refines facial models over time. In this work, a method called, Vector Quantized Optimal Stage-wise Rejecting Classifiers (VQOS-RC) for human face recognition is presented. The VQOS-RC method, works by extracting the video frames from video signals and by classifying them, using Vector Quantized Frame Rejection (VQFR) Module and Least Square Stage-wise Classifier based on an Optimal Reject Threshold factor. The VQFR module rejects unreliable classified video frames using Optimal Reject Threshold evaluation algorithm for reducing false rejection rate. Finally, the Least Square Stage-wise classifier is used for classifying the features in video events thereby improving the classification accuracy. The proposed VQOS-RC technique is analysed in terms of false rejection rate, classification time, classification accuracy. the performance results of VQOS-RC is reduced in terms of false rejection rate by 20% and classification time by 23% and improved the classification accuracy by 13% compared to state-of-art-works.